Abstract:

Conventional classification and regression tree (CART) can only increase the cognition of new categories by retraining the entire model, causing a great increase in training costs when the number of sample categories is large. To solve this problem, a lightweight incremental ensemble learning algorithm is proposed. When new categories enter the training set, we can classify those new categories by only adding CART base classifiers with the ability of open set recognition into the original ensemble learning algorithm. No retraining is required, so the computational complexity is reduced and the learning process is simplified. In the simulation experiments with the background of emitter classification, the results show that this algorithm can maintain the classification accuracy of more than 90% when the signal noise ratio equal to or larger than -4 dB. In the case of a large number of categories to be classified, this algorithm can significantly reduce the training cost compared with conventional CART.

Key words: classification and regression tree (CART), computational complexity, open set recognition, ensemble learning, emitter classification